The biases and trends in fault zone hydrogeology conceptual models: global compilation and categorical data analysis

Date

2016

Authors

Scibek, J.
Gleeson, Tom
McKenzie, J.M.

Journal Title

Journal ISSN

Volume Title

Publisher

Geofluids

Abstract

To investigate the biases and trends in observations of the permeability structures of fault zones in various geoscience disciplines, we review and compile a database of published studies and reports containing more than 900 references. The global data are categorized, mapped, and described statistically. We use the chi-square test for the dependency of categorical variables to show that the simplified fault permeability structure (barrier, conduit, barrier–conduit) depends on the observation method, geoscience discipline, and lithology. In the crystalline rocks, the in situ test methods (boreholes or tunnels) favor the detection of permeable fault conduits, in contrast to the outcrop-based measurements that favor a combined barrier–conduit conceptual models. These differences also occur, to a lesser extent, in sedimentary rocks. We provide an estimate of the occurrence of fault conduits and barriers in the brittle crust. Faults behave as conduits at 70% of sites, regardless of their barrier behavior that may also occur. Faults behave as barriers at at least 50% of the sites, in addition to often being conduits. Our review of published data from long tunnels suggests that in crystalline rocks, 40–80% (median about 60%) of faults are highly permeable conduits, and 30–70% in sedimentary rocks. The trends with depth are not clear, but there are less fault conduits counted in tunnels at the shallowest depths. The barrier hydraulic behavior of faults is more uncertain and difficult to observe than the conduit.

Description

Keywords

fault zone, hydrogeology, permeability, statistics, structural geology, tunneling

Citation

Scibek, J., Gleeson T. & McKenzie J.M. (2016). The biases and trends in fault zone hydrogeology conceptual models: global compilation and categorical data analysis. Geofluids, 16(4), 782-798.